https://github.com/jaffarkeikei/hackbio
This repository contains a comprehensive toolkit for analyzing single-cell perturbation data from the NeurIPS 2023 Competition. The project focuses on understanding how different drugs (small molecules) affect various cell types at the gene expression level.
https://github.com/jaffarkeikei/hackbio
csv-files jupyter-notebook matplotlib numpy python tensors
Last synced: 10 months ago
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This repository contains a comprehensive toolkit for analyzing single-cell perturbation data from the NeurIPS 2023 Competition. The project focuses on understanding how different drugs (small molecules) affect various cell types at the gene expression level.
- Host: GitHub
- URL: https://github.com/jaffarkeikei/hackbio
- Owner: jaffarkeikei
- Created: 2025-03-25T01:35:02.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-03-25T02:19:15.000Z (11 months ago)
- Last Synced: 2025-03-25T02:35:24.981Z (11 months ago)
- Topics: csv-files, jupyter-notebook, matplotlib, numpy, python, tensors
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# HackBio - Gene Expression Analysis
This repository contains code for analyzing and modeling gene expression data, with a focus on predicting drug responses based on gene expression patterns.
## Repository Structure
```
HackBio/
├── data/ # Data files
│ ├── raw/ # Raw data files
│ └── processed/ # Processed data files
├── docs/ # Documentation
│ └── reports/ # Project reports and findings
├── logs/ # Log files
├── notebooks/ # Jupyter notebooks
├── results/ # Results from model runs
│ ├── advanced/ # Results from advanced model
│ ├── enhanced/ # Results from enhanced model
│ ├── optimized/ # Results from optimized model
│ └── svd/ # SVD analysis results
├── src/ # Source code
│ ├── models/ # Model implementations
│ ├── preprocessing/ # Data preprocessing code
│ ├── utils/ # Utility functions
│ └── visualization/ # Data visualization code
└── tests/ # Test files
```
## Installation
```bash
# Create a virtual environment
python -m venv venv
# Activate the virtual environment
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
```
## Usage
1. Preprocess data:
```bash
python src/preprocessing/svd_optimization.py
```
2. Train and evaluate models:
```bash
python src/models/advanced_model.py
python src/models/enhanced_cnn.py
python src/models/optimized_cnn.py
```
3. View results in the `results/` directory
## Documentation
See the `docs/` directory for detailed reports and findings.